The internet continues to expand as a source of information gathering and information distribution. Businesses increasingly market, sell, support, and offer information about products to potential customers via the internet. To provide marketing support to businesses, approaches have been developed which provide information about how business' web sites are used. Data corresponding to web site use is then stored in a database, so that the data can later be analyzed. Such systems gather a tremendous amount of information and presenting all of the information, even in aggregated form, can easily overwhelm users. Furthermore, details, trends, and patters can be lost when presenting all of the collected information, further obscuring relevant marketing information.
Relevancy, especially in marketing, sales, or support environments changes frequently. The changes are sometimes influenced on a per-person, per quarter, per product, etc. basis. For example, a sales person might deem certain contracts between clients and the sales person as relevant when during that quarter, the sales person's client visited a web site 3 or more times during the quarter, remained on the website for at least 5 minutes, and had downloaded sales material.
One prior approach providing end-user access to data stored in a database is to allow an end-user to define a simple query on a single database table within a database. For example, suppose that a sales person has access to a database where one of the tables is a contacts table. In order to determine, for example, database entries from a contacts table with a last name that starts with “Fr” the Structured Query Language (SQL) query would take the form of:                SELECT * FROM contacts WHERE Last_Name=“Fr”The query illustrates that the only names available correspond directly with the field “Last_Name” in the database table “Contacts” for the object type in question (contacts in the example above). In other words each field name has a direct correspondent in the database table. However, in a system with a multitude of tables, some populated by end users but many generated from data collected by backend processors, email subsystems, interfaces to ad placement, bid management, tracking systems, etc., the types of query necessary to generate end user relevant data are much more complex.        